Dynamic Segmentation Analysis for Expedition Services: Integrating K-Means and Decision Tree
Abstract
Technological developments have an impact on increasing the level of competition between companies in acquiring and retaining customers. With this competition, companies must maximise efforts to reach consumers and understand customer service needs so that the business can continue to survive and experience development. In this effort, a segmentation analysis was carried out on marketplace accounts and expedition services commonly used by consumers to make transactions. The first step is to correct the dataset obtained to avoid errors in the final results. Next, data processing was done using rapidminer with the k-means clustering and decision tree methods. The research results show that k-means clustering achieved the lowest Davies Bouldin Index (DBI) accuracy, namely -0.943 in cluster_8. In the results of research using the decision tree method, accuracy results were obtained at 49.83%. The results obtained with the decision tree method cannot be said to be good because the results are below the 50% value; however, the decision tree method shows that a good cluster is cluster_7. In this case, better accuracy values can be achieved by using the k-means clustering method. This research can illustrate the importance of utilizing the k-means and decision tree algorithms in classifying sales data as a tool for optimizing marketing and service efforts.
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